Regional financial efficiency and its non-linear effects on economic growth in China

Hu, May; Zhang, Jing; Chao, Chi‐Chur

doi:10.1016/j.iref.2018.08.019

Cited by 53 publications

(25 citation statements)

References 42 publications

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“…DEA is an analytical method that studies efficiency problems within operational research, drawing from concepts in management and mathematical economics. Charnes, Cooper, and Rhodes (1978) put forward the first DEA model of C 2 R, which had the following advantages: (a) it can be applied to the efficiency evaluation of multi-input and multi-output; (b) it is not affected by the dimensions of input and output elements; (c) as an objective evaluation method, it is less sensitive to human subjectivity.. Due to these advantages, the DEA model is widely used in economic development (Hu, Zhang, & Chao, 2019), ecological construction (Deilmann, Lehmann, Reißmann, & Hennersdorf, 2016), scientific and technological innovation (Wang, Zhang, Fan, Lu, & Yang, 2019), and other fields. However, the traditional DEA model cannot account for the active decision-making units within the RIS, which may lead to incorrect results.…”

Section: Estimation Of Riementioning

confidence: 99%

Can regional collaborative innovation improve innovation efficiency? An empirical study of Chinese cities

2019

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The relationship between collaborative innovation and innovation efficiency has attracted the attention of researchers in recent years. However, few studies have integrated intra‐regional and inter‐regional collaborative innovations (IRCI) into a unified framework to analyze the overall impact of regional innovation efficiency. To fill this gap, this paper uses an improved Data Envelopment Analysis Model to measure the innovation efficiency of Chinese cities from 2003 to 2016 based on the regional innovation capability. Using the Coupling Coordination Degree Model to measure the degree of intra‐regional collaborative innovation, we constructed a Capability Structure Model to measure the degree of IRCI, then used the Spatial Durbin Model to empirically analyze the influence of intra‐regional and IRCI on regional innovation efficiency. The results show that: (a) both intra‐regional and IRCI promote regional innovation efficiency, but the internal factors are the primary influences on regional innovation efficiency; (b) intra‐regional collaborative innovation not only promotes local regional innovation efficiency but also promotes innovation efficiency in other regions effectively, although its driving effect on the local region is higher than on other regions; (c) there is a time lag in promoting regional innovation efficiency through the cooperation and interaction among innovation in knowledge, technological, industrial, and service, and environment capabilities. The regional innovation capacities can result in good collaborative innovation effects only after a certain period of cooperation.

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Section: Estimation Of Riementioning

confidence: 99%

Can regional collaborative innovation improve innovation efficiency? An empirical study of Chinese cities

2019

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“…Envelopment Analysis (DEA) models. DEA is a comprehensive and accepted approach used to evaluate performance in the banking industry; this method is widely accepted and have been applied in many applications (Emrouznejad and Yang, 2018), mainly because of multiple inputs and outputs used in this model and its appropriateness for examining nonlinear relationships in analyses (Chang et al, 2011, Hu et al, 2019.…”

Section: One Of the Most Effective And Widely Used Performance Evaluamentioning

confidence: 99%

Comprehensive performance evaluation of banking branches: A three-stage slacks-based measure (SBM) data envelopment analysis

Mahmoudabadi

Emrouznejad

2019

International Review of Economics & Finance

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The productivity of the banks in any country is a key factor in the growth and development of that country's economy. Recently, the evaluation and improvement of the productivity of the banking industry has been taken into much consideration in Iran. Data Envelopment Analysis (DEA) is a comprehensive and accepted approach for assessing the performance of banking industry. Although extensive studies have been done on banking industry using standard DEA models, they are, in fact, they ignore the internal structure of bank performance. Since the overall operational process of the banking system is made up of several partial processes, network DEA models are used to take into account all the internal components of the process and the coherence of the whole process. This is also done as the evaluation of the efficiency of partial processes helps to identify the sources of inefficiency of the overall banking system. In the present study, a network Slacks-Based Measure (SBM) DEA model is used in which the efficiency of the overall system is equal to the weighted average of the efficiency of the individual stages. The main advantage of this model is its ability to provide better efficiency criteria, calculate the weight of each stages separately, and simultaneously evaluate the mediator variables as both input and output. Finally, the comprehensive performance evaluation of banking industry is designed in three divisions, namely, production, intermediation, and social welfare approach. The model is applied to simultaneously evaluate operational efficiency, service effectiveness, and social effectiveness for 37 branches of one of the largest commercial banks in Iran.

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“…The development of low‐carbon industries and the increase of their efficiency are important to a country's economic growth and sustainability (Campiglio, ; Hu, Zhang, & Chao, ; Lee, Hashim, Ho, Fan, & Klemeš, ). Access to finance is critical for the development of low‐carbon industries.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Specifically, the literature has largely focused on energy and TE (e.g., Du & Mao, ; Lee & Zhang, ; Zhu, Niu, Ruth, & Shi, ), implementing low‐carbon technologies to improve economic efficiency (e.g., Gillingham & Sweeney, ; Jenkins, ), and the effectiveness of low‐carbon investments and financing from financial institutions (e.g., Campiglio, ; Hanson & Laitner, ; Kameyama, Morita, & Kubota, ; Mazzucato & Semieniuk, ; Polzin, ). Unfortunately, performance indicators used for low‐carbon industries have often been criticized for being inadequate and not conducive to analysing efficiency (Hoffmann & Busch, ; Hu et al, ; Lee et al, ). Although prior literature has also identified both macroeconomic conditions (e.g., interest rate, regulatory conditions, accounting system, banking structure, and accessibility of banking services) and firm‐specific characteristics (e.g., ownership, size, scale, financial capital, and liquidity ratio) determine a firm's financing efficiency (e.g., Altunbas, Liu, Molyneux, & Seth, ; Dietsch & Lozano‐Vivas, ; Fries & Taci, ; Nan & Wen, ; Zeng, Jiang, Ma, & Su, ), little evidence is available on the actual level of financing efficiency of low‐carbon companies.…”

Section: Literature Reviewmentioning

confidence: 99%

A quantitative study of financing efficiency of low‐carbon companies: A three‐stage data envelopment analysis

Liu

Gao

2019

Bus Strat Env

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This study set out to evaluate the financing efficiency of low-carbon companies.Applying a three-stage data envelopment analysis with the data from 85 listed companies in China's low-carbon industries over the period 2011 to 2017, this study has found that the overall financing efficiency of low-carbon companies was relatively high, and the pure technical efficiency was quite steady over the period. The overall financing efficiency of these low-carbon companies on average tended to change with the scale efficiency. This study has also shown that the scale efficiency was the main constraint influencing the financing efficiency of low-carbon companies in China over the period. Our results are robust and have significant implications for policy makers and corporate managers.

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Regional financial efficiency and its non-linear effects on economic growth in China

Cited by 53 publications

References 42 publications

Can regional collaborative innovation improve innovation efficiency? An empirical study of Chinese cities

Can regional collaborative innovation improve innovation efficiency? An empirical study of Chinese cities

Comprehensive performance evaluation of banking branches: A three-stage slacks-based measure (SBM) data envelopment analysis

A quantitative study of financing efficiency of low‐carbon companies: A three‐stage data envelopment analysis

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